Distribution of allele frequencies and effect sizes and their interrelationships for common genetic susceptibility variants
Open accessAbstract
Recent discoveries of hundreds of common susceptibility SNPs from genome-wide association studies provide a unique opportunity to examine population genetic models for complex traits. In this report, we investigate distributions of various population genetic parameters and their interrelationships using estimates of allele frequencies and effect-size parameters for about 400 susceptibility SNPs across a spectrum of qualitative and quantitative traits. We calibrate our analysis by statistical power for detection of SNPs to account for overrepresentation of variants with larger effect sizes in currently known SNPs that are expected due to statistical power for discovery. Across all qualitative disease traits, minor alleles conferred “risk” more often than “protection.” Across all traits, an inverse relationship existed between “regression effects” and allele frequencies. Both of these trends were remarkably strong for type I diabetes, a trait that is most likely to be influenced by selection, but were modest for other traits such as human height or late-onset diseases such as type II diabetes and cancers. Across all traits, the estimated effect-size distribution suggested the existence of increasingly large numbers of susceptibility SNPs with decreasingly small effects. For most traits, the set of SNPs with intermediate minor allele frequencies (5–20%) contained an unusually small number of susceptibility loci and explained a relatively small fraction of heritability compared with what would be expected from the distribution of SNPs in the general population. These trends could have several implications for future studies of common and uncommon variants.
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Analyses of risk allele frequency distributions across eight traits indicate minor allele frequencies are if anything skewed above 0.2, providing strong empirical evidence that rare alleles are not solely responsible.
“analyses of the distribution of risk allele frequencies across 8 traits argues that if anything minor allele frequencies are skewed to be greater than 0.2, providing strong empirical evidence that rare alleles are not alone responsible53.”
From Rare and common variants: twenty arguments (2012) · cites this paper as [53]
Some fraction of total risk for any complex disease is due to rare alleles, but these studies argue it is not the majority.
“No-one doubts that some fraction of the total risk for any complex disease is due to rare alleles, but these studies argue that it is not the majority.”
From Rare and common variants: twenty arguments (2012) · cites this paper as [53]
In several instances where SNP-wise GWAS found few loci, a weighted polygenic score approach provided clear evidence for polygenic risk, but the scores are noisy and capture over an order of magnitude less variance and are far from predictive.
“In several instances where SNP-wise GWAS has uncovered few loci, this approach has provided clear evidence for polygenic risk, but it must be recognized that the high variance in estimation of effects results in noisy scores that, although highly significant in the replication sample, only capture over an order of magnitude less of the variance and are far from predictive.”
From Rare and common variants: twenty arguments (2012) · cites this paper as [53]
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Cites
The genetical theory of natural selection
Fisher, R. A. (1930) · doi:10.5962/bhl.title.27468 · Cited by 17,617 · see citing works
The Neutral Theory of Molecular Evolution
Kimura, M. (1979) · Scientific American · doi:10.1038/scientificamerican1179-98 · Cited by 7,698 · see citing works
The Neutral Theory of Molecular Evolution
Kimura, M. (1983) · Cambridge University Press eBooks · doi:10.1017/cbo9780511623486 · Cited by 6,300 · see citing works
Common polygenic variation contributes to risk of schizophrenia and bipolar disorder
The International Schizophrenia Consortium (2009) · Nature · doi:10.1038/nature08185 · Cited by 4,821 · see citing works
Common SNPs explain a large proportion of the heritability for human height
Yang, J., Benyamin, B., McEvoy, B. P., Gordon, S., Henders, A. K., Nyholt, D. R., Madden, P. A., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2010) · Nature Genetics · doi:10.1038/ng.608 · Cited by 4,446 · see citing works
A generalization of sampling without replacement from a finite universe
Horvitz, D. G., & Thompson, D. J. (1952) · Journal of the American Statistical Association · doi:10.1080/01621459.1952.10483446 · Cited by 4,293 · see citing works
Biological, clinical and population relevance of 95 loci for blood lipids
Teslovich, T. M., Musunuru, K., Smith, A. V., Edmondson, A. C., Stylianou, I. M., Koseki, M., Pirruccello, J. P., Ripatti, S., Chasman, D. I., Willer, C. J., et al. (2010) · Nature · doi:10.1038/nature09270 · Cited by 3,487 · see citing works
A Generalization of Sampling Without Replacement From a Finite Universe
Horvitz, D. G., & Thompson, D. J. (1952) · Journal of the American Statistical Association · doi:10.2307/2280784 · Cited by 3,011 · see citing works
Franke, A., McGovern, D. P. B., Barrett, J. C., Wang, K., Radford-Smith, G. L., Ahmad, T., Lees, C. W., Balschun, T., Lee, J., Roberts, R., et al. (2010) · Nature Genetics · doi:10.1038/ng.717 · Cited by 2,520 · see citing works
Hundreds of variants clustered in genomic loci and biological pathways affect human height
Lango Allen, H., Estrada, K., Lettre, G., Berndt, S. I., Weedon, M. N., Rivadeneira, F., Willer, C. J., Jackson, A. U., Vedantam, S., Raychaudhuri, S., et al. (2010) · Nature · doi:10.1038/nature09410 · Cited by 1,945 · see citing works
Self-citationTwelve type 2 diabetes susceptibility loci identified through large-scale association analysis
The MAGIC investigators, Voight, B. F., The GIANT Consortium, Scott, L. J., Steinthorsdottir, V., Morris, A. P., Dina, C., Welch, R. P., Zeggini, E., Huth, C., et al. (2010) · Nature Genetics · doi:10.1038/ng.609 · Cited by 1,761 · see citing works
The Type 1 Diabetes Genetics Consortium, Barrett, J. C., Clayton, D. G., Concannon, P., Akolkar, B., Cooper, J. D., Erlich, H. A., Julier, C., Morahan, G., Nerup, J., et al. (2009) · Nature Genetics · doi:10.1038/ng.381 · Cited by 1,746 · see citing works
Are Rare Variants Responsible for Susceptibility to Complex Diseases?
Pritchard, J. K. (2001) · The American Journal of Human Genetics · doi:10.1086/321272 · Cited by 1,197 · see citing works
Polygenic susceptibility to breast cancer and implications for prevention
Pharoah, P. D. P., Antoniou, A. C., Bobrow, M., Zimmern, R. L., Easton, D. F., & Ponder, B. A. J. (2002) · Nature Genetics · doi:10.1038/ng853 · Cited by 898 · see citing works
THE POPULATION GENETICS OF ADAPTATION: THE DISTRIBUTION OF FACTORS FIXED DURING ADAPTIVE EVOLUTION
Orr, H. A. (1998) · Evolution · doi:10.1111/j.1558-5646.1998.tb01823.x · Cited by 895 · see citing works
Genome-wide association study identifies five susceptibility loci for glioma
Shete, S. S., Hosking, F. J., Robertson, L. B., Dobbins, S. E., Sanson, M., Malmer, B., Simon, M., Marie, Y., Boisselier, B., Delattre, J., et al. (2009) · Nature Genetics · doi:10.1038/ng.407 · Cited by 777 · see citing works
Genome-wide association study identifies five new breast cancer susceptibility loci
The Breast Cancer Susceptibility Collaboration (UK), Turnbull, C., Ahmed, S., Morrison, J., Pernet, D., Renwick, A., Maranian, M., Seal, S., Ghoussaini, M., Hines, S., et al. (2010) · Nature Genetics · doi:10.1038/ng.586 · Cited by 687 · see citing works
Park, J.-H., Wacholder, S., Gail, M. H., Peters, U., Jacobs, K. B., Chanock, S. J., & Chatterjee, N. (2010) · Nature Genetics · doi:10.1038/ng.610 · Cited by 678 · see citing works
Self-citationPetersen, G. M., Amundadottir, L. T., Fuchs, C. S., Kraft, P., Stolzenberg‐Solomon, R. Z., Jacobs, K. B., Arslan, A. A., Bueno-de-Mesquita, H. B., Gallinger, S., Gross, M. D., et al. (2010) · Nature Genetics · doi:10.1038/ng.522 · Cited by 596 · see citing works
Self-citationStudy, C. (2008) · Nature Genetics · doi:10.1038/ng.262 · Cited by 558 · see citing works
Epidemiologic studies utilizing surveys: accounting for the sampling design.
Korn, E. L., & Graubard, B. I. (1991) · American Journal of Public Health · doi:10.2105/ajph.81.9.1166 · Cited by 485 · see citing works
Rothman, N., García‐Closas, M., Chatterjee, N., Malats, N., Wu, X., Figueroa, J. D., Real, F. X., Berg, D. V. D., Matullo, G., Baris, D., et al. (2010) · Nature Genetics · doi:10.1038/ng.687 · Cited by 481 · see citing works
Self-citationEeles, R. A., Collaborators, T. U. P. S., Kote‐Jarai, Z., Olama, A. A. A., Giles, G. G., Guy, M., Severi, G., Muir, K. R., Hopper, J. L., Henderson, B. E., et al. (2009) · Nature Genetics · doi:10.1038/ng.450 · Cited by 418 · see citing works
Self-citationPaajanen, T. A., Oksala, N., Kuukasjärvi, P., & Karhunen, P. J. (2010) · European Heart Journal · doi:10.1093/eurheartj/ehq155 · Cited by 336 · see citing works
Eyre‐Walker, A. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0906182107 · Cited by 231 · see citing works
Lifestyle factors and primary glioma and meningioma tumours in the Million Women Study cohort
Collaborators, F. T. M. W. S., Benson, V. S., Pirie, K. L., Green, J., Casabonne, D., & Beral, V. (2008) · British Journal of Cancer · doi:10.1038/sj.bjc.6604445 · Cited by 199 · see citing works
Theories of adaptation: what they do and don?t say
Orr, H. A. (2005) · Genetica · doi:10.1007/s10709-004-2702-3 · Cited by 119 · see citing works
Theories of adaptation: what they do and don’t say
Orr, H. A. (2005) · Springer eBooks · doi:10.1007/1-4020-3836-4_2 · Cited by 110 · see citing works
Height, Body Mass Index, and Physical Activity in Relation to Glioma Risk
Moore, S. C., Rajaraman, P., Dubrow, R., Darefsky, A. S., Koebnick, C., Hollenbeck, A. R., Schatzkin, A. G., & Leitzmann, M. F. (2009) · Cancer Research · doi:10.1158/0008-5472.can-09-1669 · Cited by 105 · see citing works
The Distribution of Gene Frequencies in Populations of Polyploids
Wright, S. (1938) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.24.9.372 · Cited by 70 · see citing works
Estimating Odds Ratios in Genome Scans: An Approximate Conditional Likelihood Approach
Ghosh, A., Zou, F., & Wright, F. A. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2008.03.002 · Cited by 66 · see citing works
Tallness is associated with risk of testicular cancer: evidence for the nutrition hypothesis
for German Testicular Cancer Study Group, Dieckmann, K.-P., Hartmann, J. T., Claßen, J., Lüdde, R., Diederichs, M., & Pichlmeier, U. (2008) · British Journal of Cancer · doi:10.1038/sj.bjc.6604695 · Cited by 32 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Rare and common variants: twenty arguments
Gibson, G. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3118 · Cited by 1,278 · see citing works
The UK10K project identifies rare variants in health and disease
The UK10K Consortium, Writing group, Walter, K., Min, J. L., Huang, J., Crooks, L., Memari, Y., McCarthy, S., Perry, J. R. B., Xu, C., et al. (2015) · Nature · doi:10.1038/nature14962 · Cited by 1,040 · see citing works
Improved Heritability Estimation from Genome-wide SNPs
Speed, D., Hemani, G., Johnson, M. R., & Balding, D. J. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.10.010 · Cited by 700 · see citing works
Comprehensive genotyping of the USA national maize inbred seed bank
Romay, M. C., Millard, M. J., Glaubitz, J. C., Peiffer, J. A., Swarts, K., Casstevens, T. M., Elshire, R. J., Acharya, C. B., Mitchell, S. E., Flint-Garcia, S. A., et al. (2013) · Genome biology · doi:10.1186/gb-2013-14-6-r55 · Cited by 546 · see citing works
Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program
Verma, A., Huffman, J. E., Rodriguez, A., Conery, M., Liu, M., Ho, Y.-L., Kim, Y., Heise, D. A., Guare, L., Panickan, V. A., et al. (2024) · Science · doi:10.1126/science.adj1182 · Cited by 463 · see citing works
Self-citationZhang, J., Dutta, D., Köttgen, A., Tin, A., Schlosser, P., Grams, M. E., Harvey, B., CKDGen Consortium, Yu, B., Boerwinkle, E., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01051-w · Cited by 373 · see citing works
Self-citationSignatures of negative selection in the genetic architecture of human complex traits
Zeng, J., de Vlaming, R., Wu, Y., Robinson, M. R., Lloyd-Jones, L. R., Yengo, L., Yap, C. X., Xue, A., Sidorenko, J., McRae, A. F., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0101-4 · Cited by 391 · see citing works
Selecting instruments for Mendelian randomization in the wake of genome-wide association studies.
Swerdlow, D. I., Kuchenbaecker, K. B., Shah, S., Sofat, R., Holmes, M. V., White, J., Mindell, J. S., Kivimaki, M., Brunner, E. J., Whittaker, J. C., et al. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw088 · Cited by 387 · see citing works
Tsoi, L. C., Stuart, P. E., Tian, C., Gudjonsson, J. E., Das, S., Zawistowski, M., Ellinghaus, E., Barker, J. N., Chandran, V., Dand, N., et al. (2017) · Nature Communications · doi:10.1038/ncomms15382 · Cited by 384 · see citing works
The Fourth Law of Behavior Genetics
Chabris, C. F., Lee, J. J., Cesarini, D. A., Benjamin, D. J., & Laibson, D. (2015) · Current Directions in Psychological Science · doi:10.1177/0963721415580430 · Cited by 361 · see citing works
Chatterjee, N., Wheeler, B., Sampson, J. N., Hartge, P. A., Chanock, S. J., & Park, J.-H. (2013) · Nature Genetics · doi:10.1038/ng.2579 · Cited by 351 · see citing works
Self-citationThe impact of rare and low-frequency genetic variants in common disease
Bomba, L., Walter, K., & Soranzo, N. (2017) · Genome biology · doi:10.1186/s13059-017-1212-4 · Cited by 342 · see citing works
MacLeod, I. M., Bowman, P. J., Jagt, C. J. V., Haile‐Mariam, M., Kemper, K. E., Chamberlain, A. J., Schrooten, C., Hayes, B. J., & Goddard, M. E. (2016) · BMC Genomics · doi:10.1186/s12864-016-2443-6 · Cited by 328 · see citing works
Dapas, M., Lin, F. T. J., Nadkarni, G. N., Sisk, R., Legro, R. S., Urbanek, M., Hayes, M. G., & Dunaif, A. (2020) · PLOS Medicine · doi:10.1371/journal.pmed.1003132 · Cited by 297 · see citing works
Genetic basis of chronotype in humans: insights from three landmark GWAS
Kalmbach, D. A., Schneider, L. D., Cheung, J., Bertrand, S. J., Kariharan, T., Pack, A. I., & Gehrman, P. R. (2017) · Sleep · doi:10.1093/sleep/zsw048 · Cited by 243 · see citing works
Druet, T., MacLeod, I. M., & Hayes, B. J. (2013) · Heredity · doi:10.1038/hdy.2013.13 · Cited by 213 · see citing works
High Trans-ethnic Replicability of GWAS Results Implies Common Causal Variants
Marigorta, U. M., & Navarro, A. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003566 · Cited by 207 · see citing works
Behavior genetics and postgenomics
Charney, E. (2012) · Behavioral and Brain Sciences · doi:10.1017/s0140525x11002226 · Cited by 207 · see citing works
Ni, G., Zeng, J., Revez, J. A., Wang, Y., Zheng, Z., Ge, T., Restuadi, R., Kiewa, J., Nyholt, D. R., Coleman, J. R. I., et al. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2021.04.018 · Cited by 203 · see citing works
Shrine, N. R. G., Izquierdo, A. G., Chen, J., Packer, R. J., Hall, R. J., Guyatt, A. L., Batini, C., Thompson, R. J., Pavuluri, C. K., Malik, V., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01314-0 · Cited by 191 · see citing works
Pirola, C. J., & Sookoian, S. C. (2015) · Hepatology · doi:10.1002/hep.28142 · Cited by 171 · see citing works
Genetic Risk Factors Associated With Preeclampsia and Hypertensive Disorders of Pregnancy
Tyrmi, J. S., Kaartokallio, T., Lokki, A. I., Jääskeläinen, T. J., Kortelainen, E., Ruotsalainen, S., Karjalainen, J. M., Ripatti, S., Kivioja, A., Laisk, T., et al. (2023) · JAMA Cardiology · doi:10.1001/jamacardio.2023.1312 · Cited by 160 · see citing works
Heritability in the genome-wide association era
Zaitlen, N., & Kraft, P. L. (2012) · Human Genetics · doi:10.1007/s00439-012-1199-6 · Cited by 156 · see citing works
The Impact of Population Demography and Selection on the Genetic Architecture of Complex Traits
Lohmueller, K. E. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004379 · Cited by 152 · see citing works
Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots
Agrawal, S., Wang, M., Klarqvist, M. D. R., Smith, K., Shin, J. H., Dashti, H., Diamant, N., Choi, S. H., Jurgens, S. J., Ellinor, P. T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30931-2 · Cited by 152 · see citing works
Explaining additional genetic variation in complex traits
Robinson, M. R., Wray, N. R., & Visscher, P. M. (2014) · Trends in Genetics · doi:10.1016/j.tig.2014.02.003 · Cited by 147 · see citing works
Evaluating empirical bounds on complex disease genetic architecture
Agarwala, V., Flannick, J. A., Sunyaev, S. R., & Altshuler, D. (2013) · Nature Genetics · doi:10.1038/ng.2804 · Cited by 143 · see citing works
Whole‐exome sequencing in 20,197 persons for rare variants in Alzheimer's disease
Raghavan, N. S., Brickman, A. M., Andrews, H., Manly, J. J., Schupf, N., Lantigua, R., Wolock, C. J., Kamalakaran, S., Petrovski, S., Tosto, G., et al. (2018) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.582 · Cited by 141 · see citing works
Genetic–epigenetic interactions in cis: a major focus in the post-GWAS era
Do, C., Shearer, A., Suzuki, M., Terry, M. B., Gelernter, J. E., Greally, J. M., & Tycko, B. (2017) · Genome biology · doi:10.1186/s13059-017-1250-y · Cited by 141 · see citing works
Genetic architecture of Alzheimer's disease
Neuner, S. M., TCW, J., & Goate, A. M. (2020) · Neurobiology of Disease · doi:10.1016/j.nbd.2020.104976 · Cited by 126 · see citing works
Mechanisms and Disease Associations of Haplotype-Dependent Allele-Specific DNA Methylation
Do, C., Lang, C. F., Lin, J. P., Darbary, H. K., Krupska, I., Gaba, A., Petukhova, L., Vonsattel, J., GALLAGHER, M. P., Goland, R., et al. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.03.027 · Cited by 125 · see citing works
Sinclair‐Waters, M., Ødegård, J., Korsvoll, S. A., Moen, T., Lien, S., Primmer, C. R., & Barson, N. J. (2020) · Genetics Selection Evolution · doi:10.1186/s12711-020-0529-8 · Cited by 118 · see citing works
Genetics: Implications for Prevention and Management of Coronary Artery Disease
Assimes, T. L., & Roberts, R. (2016) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2016.10.039 · Cited by 116 · see citing works
Genetic Determinants Influencing Human Serum Metabolome among African Americans
Yu, B., Zheng, Y., Alexander, D. C., Morrison, A. C., Coresh, J., & Boerwinkle, E. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004212 · Cited by 102 · see citing works
APLP2 Regulates Refractive Error and Myopia Development in Mice and Humans
Tkatchenko, A. V., Tkatchenko, T. V., Guggenheim, J. A., Verhoeven, V. J. M., Hysi, P. G., Wojciechowski, R., Singh, P. K., Kumar, A., Thinakaran, G., & Williams, C. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1005432 · Cited by 98 · see citing works
MacLeod, I. M., Hayes, B. J., & Goddard, M. E. (2014) · Genetics · doi:10.1534/genetics.114.168344 · Cited by 97 · see citing works
TEC Monitoring by Using Satellite Positioning Systems
Jakowski, N. (1996) · elib (German Aerospace Center) · Cited by 97 · see citing works
Rare variants contribute disproportionately to quantitative trait variation in yeast
Bloom, J. S., Boocock, J., Treusch, S., Sadhu, M. J., Day, L., Oates-Barker, H., & Kruglyak, L. (2019) · eLife · doi:10.7554/elife.49212 · Cited by 93 · see citing works
Are minor alleles more likely to be risk alleles?
隆, 城., Sikora-Wohlfeld, W., Kawashima, M., Kikuchi, S., Kamatani, N., Patwardhan, A. J., Chen, R., Sirota, M., Kodama, K., Hadley, D., et al. (2018) · BMC Medical Genomics · doi:10.1186/s12920-018-0322-5 · Cited by 84 · see citing works
Genetics of Callous-Unemotional Behavior in Children
Viding, E., Price, T. S., Jaffee, S. R., Trzaskowski, M., Davis, O., Meaburn, E., Haworth, C., & Plomin, R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0065789 · Cited by 74 · see citing works
Personalized medicine: Genetic risk prediction of drug response
Zhang, G., & Nebert, D. W. (2017) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2017.02.036 · Cited by 78 · see citing works
The genetic architecture of biological age in nine human organ systems
Wen, J., Tian, Y., Skampardoni, I., Yang, Z., Cui, Y., Anagnostakis, F., Mamourian, E., Zhao, B., Toga, A. W., Zalesky, A., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00662-8 · Cited by 73 · see citing works
Squires, K. E., Montañez‐Miranda, C., Pandya, R. R., Torres, M. P., & Hepler, J. R. (2018) · Pharmacological Reviews · doi:10.1124/pr.117.015354 · Cited by 71 · see citing works
Shared Genetic Control of Root System Architecture between Zea mays and Sorghum bicolor
Zheng, Z., Hey, S., Jubery, T. Z., Liu, H., Yu, Y., Coffey, L., Miao, C., Sigmon, B., Schnable, J. C., Hochholdinger, F., et al. (2019) · PLANT PHYSIOLOGY · doi:10.1104/pp.19.00752 · Cited by 70 · see citing works
Evolutionary perspectives on polygenic selection, missing heritability, and GWAS
Uricchio, L. H. (2019) · Human Genetics · doi:10.1007/s00439-019-02040-6 · Cited by 66 · see citing works
Anderson, M. R., Miller, L., WICKRAMARATNE, P. J., Svob, C., Odgerel, Z., Zhao, R., & Weissman, M. M. (2017) · Spirituality in Clinical Practice · doi:10.1037/scp0000125 · Cited by 65 · see citing works
Noh, H. J., Tang, R., Flannick, J. A., O'Dushlaine, C. T., Swofford, R., Howrigan, D. P., Genereux, D. P., Johnson, J., van Grootheest, G., Grünblatt, E., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-00831-x · Cited by 65 · see citing works
Deleterious Mutation Burden and Its Association with Complex Traits in Sorghum (Sorghum bicolor)
Valluru, R., Gazave, É., Fernandes, S. B., Ferguson, J. N., Lozano, R. D., Hirannaiah, P., Zuo, T., Brown, P. J., Leakey, A. D. B., Gore, M. A., et al. (2019) · Genetics · doi:10.1534/genetics.118.301742 · Cited by 63 · see citing works
Race in Biological and Biomedical ResearchPreprint
Cooper, R. (2013) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a008573 · Cited by 62 · see citing works
Yoshida, G., & Yañez, J. M. (2021) · Evolutionary Applications · doi:10.1111/eva.13240 · Cited by 60 · see citing works
Germline Variants That Affect Tumor Progression
Chatrath, A., Ratan, A., & Dutta, A. (2020) · Trends in Genetics · doi:10.1016/j.tig.2020.10.005 · Cited by 57 · see citing works
Limitations of principal components in quantitative genetic association models for human studies
Yao, Y., & Ochoa, A. (2023) · eLife · doi:10.7554/elife.79238 · Cited by 54 · see citing works
Uricchio, L. H., Zaitlen, N., Ye, C., Witte, J. S., & Hernandez, R. D. (2016) · Genome Research · doi:10.1101/gr.202440.115 · Cited by 54 · see citing works
Eckert, A. J., Wegrzyn, J., Liechty, J. D., Lee, J. M., Cumbie, W. P., Davis, J. M., Goldfarb, B., Loopstra, C. A., Palle, S. R., Quesada, T., et al. (2013) · Genetics · doi:10.1534/genetics.113.157198 · Cited by 53 · see citing works
Zou, J., Valiant, G. J., Valiant, P., Karczewski, K. J., Chan, S. O., Samocha, K. E., Lek, M., Sunyaev, S. R., Daly, M. J., & MacArthur, D. G. (2016) · Nature Communications · doi:10.1038/ncomms13293 · Cited by 53 · see citing works
Li, Y., Wang, D. W., Chen, Y., Chen, C., Guo, J., Zhang, S., Sun, Z., Ding, H., Yao, Y., Zhou, L., et al. (2018) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.117.310594 · Cited by 50 · see citing works
Evolutionary dynamics and adaptive benefits of deleterious mutations in crop gene pools
Dwivedi, S. L., Heslop-Harrison, J. S., Spillane, C., McKeown, P. C., Edwards, D., Goldman, I. L., & Ortiz, R. O. (2023) · Trends in Plant Science · doi:10.1016/j.tplants.2023.01.006 · Cited by 49 · see citing works
Estimating Effect Sizes and Expected Replication Probabilities from GWAS Summary Statistics
Holland, D., Wang, Y., Thompson, W. K., Schork, A. J., Chen, C., Lo, M., Witoelar, A., Werge, T. M., O’Donovan, M. C., Andreassen, O. A., et al. (2016) · Frontiers in Genetics · doi:10.3389/fgene.2016.00015 · Cited by 49 · see citing works
Wolujewicz, P., Steele, J., Kaltschmidt, J. A., Finnell, R. H., & Ross, M. E. (2021) · genesis · doi:10.1002/dvg.23459 · Cited by 44 · see citing works
Genomic Analysis of Resistance to Fall Armyworm (Spodoptera frugiperda) in CIMMYT Maize Lines
Kamweru, I., Bruce, A. Y., Beyene, Y., Makumbi, D., Adetimirin, V. O., Prasanna, B. M., & Gowda, M. (2022) · Genes · doi:10.3390/genes13020251 · Cited by 43 · see citing works
Venkatesh, S. S., Wittemans, L. B. L., Palmer, D. S., Baya, N. A., Ferreira, T. C., Hill, B., Lassen, F. H., Parker, M. J., Reibe, S., Elhakeem, A., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02156-8 · Cited by 43 · see citing works
The pan-cancer landscape of prognostic germline variants in 10,582 patients
Chatrath, A., Przanowska, R. K., Kiran, S., Su, Z., Saha, S., Wilson, B., Tsunematsu, T., Ahn, J., Lee, K. Y., Paulsen, T., et al. (2020) · Genome Medicine · doi:10.1186/s13073-020-0718-7 · Cited by 43 · see citing works
Chan, Y., Lim, E. T., Sandholm, N., Wang, S. R., McKnight, A. J., Ripke, S., Daly, M. J., Neale, B. M., Salem, R. M., & Hirschhorn, J. N. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.02.006 · Cited by 43 · see citing works
Emdin, C. A., Khera, A. V., Aragam, K. G., Haas, M. E., Chaffin, M. D., Klarin, D. M., Natarajan, P., Bick, A. G., Zekavat, S. M., Nomura, A., et al. (2018) · Diabetes · doi:10.2337/db18-0857 · Cited by 42 · see citing works
Research Review: A guide to computing and implementing polygenic scores in developmental research
Allegrini, A. G., Baldwin, J. R., Barkhuizen, W., & Pingault, J. (2022) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13611 · Cited by 41 · see citing works
The 330 risk loci known for systemic lupus erythematosus (SLE): a review
Laurynenka, V., & Harley, J. B. (2024) · Frontiers in Lupus · doi:10.3389/flupu.2024.1398035 · Cited by 41 · see citing works
Genetic Influences on the Developing Young Brain and Risk for Neuropsychiatric Disorders
Alex, A. M., Buß, C., Davis, E. P., Campos, G. D. L., Donald, K. A., Fair, D. A., Gaab, N., Gao, W., Gilmore, J. H., Girault, J. B., et al. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.01.013 · Cited by 41 · see citing works
The use of vector bootstrapping to improve variable selection precision in Lasso models
Laurin, C., Boomsma, D. I., & Lubke, G. H. (2016) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2015-0043 · Cited by 40 · see citing works
Genome‐Wide Association Mapping of Host‐Plant Resistance to Soybean Aphid
Hanson, A. A., Lorenz, A. J., Hesler, L. S., Bhusal, S. J., Bansal, R., Michel, A. P., Jiang, G., & Koch, R. L. (2018) · The Plant Genome · doi:10.3835/plantgenome2018.02.0011 · Cited by 39 · see citing works
Sample Size Calculation in Genetic Association Studies: A Practical Approach
Politi, C., Roumeliotis, S. K., Tripepi, G., & Spoto, B. (2023) · Life · doi:10.3390/life13010235 · Cited by 38 · see citing works
Applying compressed sensing to genome-wide association studies
Vattikuti, S., Lee, J. J., Chang, C. C., Hsu, S., & Chow, C. C. (2014) · GigaScience · doi:10.1186/2047-217x-3-10 · Cited by 38 · see citing works
Schraiber, J. G., Edge, M. D., & Pennell, M. (2024) · PLoS Biology · doi:10.1371/journal.pbio.3002847 · Cited by 37 · see citing works
Dissection of additive genetic variability for quantitative traits in chickens using SNP markers
Abdollahi-Arpanahi, R., Pakdel, A., Nejati‐Javaremi, A., Shahrbabak, M. M., Morota, G., Valente, B. D., Kranis, A., Rosa, G. J. M., & Daniel Gianola, B. (2014) · Journal of Animal Breeding and Genetics · doi:10.1111/jbg.12079 · Cited by 37 · see citing works
Wollstein, A., Walsh, S., Liu, F., Chakravarthy, U., Rahu, M., Seland, J. H., Soubrane, G., Tomazzoli, L., Topouzis, F., Vingerling, J. R., et al. (2017) · Scientific Reports · doi:10.1038/srep43359 · Cited by 36 · see citing works
Choi, J., Kim, S., Kim, J., Son, H., Yoo, S., Kim, C.-U., Park, Y. J., Moon, S., Cha, B., Jeon, M. C., et al. (2023) · Science Advances · doi:10.1126/sciadv.adg6319 · Cited by 34 · see citing works
Self-citationEnrichment of Minor Alleles of Common SNPs and Improved Risk Prediction for Parkinson's Disease
Zhu, Z., Yuan, D., Luo, D., Lu, X., & Huang, S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0133421 · Cited by 34 · see citing works
Conditions for the validity of SNP-based heritability estimation
Lee, J. J., & Chow, C. C. (2014) · Human Genetics · doi:10.1007/s00439-014-1441-5 · Cited by 34 · see citing works
Tran, T. T. H., Nguyen, H. T., Le, B. T. N., Tran, P. H., Nguyen, S. V., & Kim, O. T. P. (2021) · Aquaculture · doi:10.1016/j.aquaculture.2021.736542 · Cited by 33 · see citing works
Cormier, H., Vigneault, J., Garneau, V., Tchernof, A., Vohl, M., Weisnagel, S. J., & Robitaille, J. (2014) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.12937 · Cited by 33 · see citing works
Yuan, D., Zhu, Z., Tan, X., Liang, J., Zeng, C., Zhang, J., Chen, J., Ma, L., Doğan, A., Brockmann, G. A., et al. (2014) · Science China Life Sciences · doi:10.1007/s11427-014-4704-4 · Cited by 33 · see citing works
Exploring the Use of Molecular Biomarkers for Precision Medicine in Age-Related Macular Degeneration
Lorés‐Motta, L., de Jong, E. K., & den Hollander, A. I. (2018) · Molecular Diagnosis & Therapy · doi:10.1007/s40291-018-0332-1 · Cited by 32 · see citing works
Age-related macular degeneration
de Jong, E. K., Geerlings, M. J., & den Hollander, A. I. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816222-4.00010-1 · Cited by 32 · see citing works
Ganna, A., Lee, D., Ingelsson, E., & Yudi Pawitan, Y. (2014) · Briefings in Bioinformatics · doi:10.1093/bib/bbu033 · Cited by 32 · see citing works
Why do Individuals Differ in Viral Susceptibility? A Story Told by Model Organisms
van Sluijs, L., Pijlman, G. P., & Kammenga, J. E. (2017) · Viruses · doi:10.3390/v9100284 · Cited by 31 · see citing works
Innate immune gene variation and differential susceptibility to uterine diseases in Holstein cows
Pinedo, P. J., Galvão, K. N., & Seabury, C. M. (2013) · Theriogenology · doi:10.1016/j.theriogenology.2013.04.027 · Cited by 30 · see citing works
Dimitrakopoulos, C. M., Vrugt, B., Flury, R., Schraml, P., Knippschild, U., Wild, P. J., Hoerstrup, S. P., Henne‐Bruns, D., Wuerl, P., Graf, R., et al. (2019) · JAMA Surgery · doi:10.1001/jamasurg.2019.0484 · Cited by 30 · see citing works
Han, S. K., McNulty, M. T., Benway, C. J., Wen, P., Greenberg, A., Onuchic-Whitford, A. C., Nephrotic Syndrome Study Network (NEPTUNE), Jang, D., Flannick, J. A., Burtt, N. P., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37691-7 · Cited by 30 · see citing works
Xie, K., Chen, M., Zhu, M., Wang, C., Qin, N., Liang, C., Song, C., Dai, J., Jin, G., Shen, H., et al. (2017) · International Journal of Cancer · doi:10.1002/ijc.30788 · Cited by 30 · see citing works
García–Álvarez, M., Berenguer, J., Jiménez‐Sousa, M. Á., Tenor, D. P., Aldámiz‐Echevarría, T., Tejerina, F., Díez, C., Vázquez‐Morón, S., & Resino, S. (2017) · Scientific Reports · doi:10.1038/srep41516 · Cited by 29 · see citing works
Karamperis, K., Katz, S., Melograna, F., Ganau, F. P., Steen, K. V., Patrinos, G. P., & Lao, Ó. (2024) · iScience · doi:10.1016/j.isci.2024.110916 · Cited by 28 · see citing works
Human Genetics and Respiratory Syncytial Virus Disease: Current Findings and Future Approaches
Choi, E. H., Lee, H. J., & Chanock, S. J. (2013) · Current topics in microbiology and immunology · doi:10.1007/978-3-642-38919-1_6 · Cited by 28 · see citing works
Self-citationMa, G., Liu, H., Du, M., Zhang, G., Lin, Y., Ge, Y., Wang, M., Jin, G., Zhao, Q., Chu, H., et al. (2019) · Cancer · doi:10.1002/cncr.32081 · Cited by 28 · see citing works
The dichotomous role of epiregulin in pain
Verma, V., Khoury, S., Parisien, M., Cho, C., Maixner, W. D., Martin, L. J., & Diatchenko, L. B. (2020) · Pain · doi:10.1097/j.pain.0000000000001792 · Cited by 28 · see citing works
Gradient Boosting as a SNP Filter: an Evaluation Using Simulated and Hair Morphology Data
Laurin, C. (2013) · Journal of Data Mining in Genomics & Proteomics · doi:10.4172/2153-0602.1000143 · Cited by 27 · see citing works
Legarra, A., Garcia-Baccino, C., Wientjes, Y. C. J., & Vitezica, Z. G. (2021) · Genetics · doi:10.1093/genetics/iyab138 · Cited by 27 · see citing works
The contribution of rare genetic variants to the pathogenesis of polycystic ovary syndrome
Dapas, M., & Dunaif, A. E. (2020) · Current Opinion in Endocrine and Metabolic Research · doi:10.1016/j.coemr.2020.02.011 · Cited by 27 · see citing works
Accumulation of minor alleles and risk prediction in schizophrenia
He, P., Lei, X., Yuan, D., Zhu, Z., & Huang, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-12104-0 · Cited by 26 · see citing works
Lin, W., Yi, N., Lou, X., Zhi, D., Zhang, K., Gao, G., Tiwari, H. K., & Liu, N. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21740 · Cited by 25 · see citing works
Strategies for Developing Prediction Models From Genome‐Wide Association Studies
Wu, J., Pfeiffer, R. M., & Gail, M. H. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21762 · Cited by 24 · see citing works
Self-citationLyra, D. H., Griffiths, C. A., Watson, A., Joynson, R., Molero, G., Igna, A., Hassani‐Pak, K., Reynolds, M. P., Hall, A. J. W., & Paul, M. J. (2021) · Food and Energy Security · doi:10.1002/fes3.292 · Cited by 24 · see citing works
- Authors
- Park, J.-H., Gail, M. H., Weinberg, C. R., Carroll, R. J., Chung, C. C., Wang, Z., Chanock, S. J., Fraumeni, J. F., & Chatterjee, N.
- Affiliations
- National Institutes of Health (US)
- Texas A&M University (US)
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2011-10-14
- Volume
- 108
- Issue
- 44
- Pages
- 18026-18031
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3207674
- OpenAlex
- W2074498980
- Cited by
- 280
- References
- 31
- Citations by year
- 2026: 10 · 2025: 14 · 2024: 21 · 2023: 23 · 2022: 16 · 2021: 26 · 2020: 21 · 2019: 22 · 2018: 22 · 2017: 24 · 2016: 13 · 2015: 17 · 2014: 19 · 2013: 22 · 2012: 9
- FWCI
- 9.84
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- OakTrust (Texas A&M University Libraries) (submittedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- Funders
- National Institutes of Health (5r37ca057030-23)
- National Cancer Institute ((R37-CA057030), U01CA057030, R27-CA057030, R01CA057030)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Keywords
- allele frequency distribution, effect size distribution, genetic risk variants, single nucleotide polymorphisms, statistical power, minor allele frequency, regression effect, type 1 diabetes, type 2 diabetes, human height, cancer susceptibility, heritability, complex traits, population genetics, natural selection
- MeSH terms
- Calibration, Gene Frequency, Humans, Genetic Variation, Polymorphism, Single Nucleotide
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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